Average Error: 0.5 → 0.5
Time: 1.6m
Precision: 64
\[x1 + \left(\left(\left(\left(\left(\left(\left(2.0 \cdot x1\right) \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right) \cdot \left(\frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0} - 3.0\right) + \left(x1 \cdot x1\right) \cdot \left(4.0 \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0} - 6.0\right)\right) \cdot \left(x1 \cdot x1 + 1.0\right) + \left(\left(3.0 \cdot x1\right) \cdot x1\right) \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right) + \left(x1 \cdot x1\right) \cdot x1\right) + x1\right) + 3.0 \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 - 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right)\]
↓
\[x1 + \left(\left(x1 + \left(x1 \cdot \left(x1 \cdot x1\right) + \left(\left(\left(\frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + x2 \cdot 2.0\right) - x1}{1.0 + x1 \cdot x1} - 3.0\right) \cdot \left(\left(x1 \cdot 2.0\right) \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + x2 \cdot 2.0\right) - x1}{1.0 + x1 \cdot x1}\right) + \left(\left(x1 \cdot x1\right) \cdot \mathsf{fma}\left(6.0, -1, 6.0\right) + \left(\left(-x1\right) \cdot \left(x1 \cdot 6.0\right) + \left(4.0 \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + x2 \cdot 2.0\right) - x1}{1.0 + x1 \cdot x1}\right) \cdot \left(x1 \cdot x1\right)\right)\right)\right) \cdot \left(1.0 + x1 \cdot x1\right) + \left(\left(3.0 \cdot x1\right) \cdot x1\right) \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + x2 \cdot 2.0\right) - x1}{1.0 + x1 \cdot x1}\right)\right)\right) + \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 - x2 \cdot 2.0\right) - x1}{1.0 + x1 \cdot x1} \cdot 3.0\right)\]
x1 + \left(\left(\left(\left(\left(\left(\left(2.0 \cdot x1\right) \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right) \cdot \left(\frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0} - 3.0\right) + \left(x1 \cdot x1\right) \cdot \left(4.0 \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0} - 6.0\right)\right) \cdot \left(x1 \cdot x1 + 1.0\right) + \left(\left(3.0 \cdot x1\right) \cdot x1\right) \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right) + \left(x1 \cdot x1\right) \cdot x1\right) + x1\right) + 3.0 \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 - 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right)
↓
x1 + \left(\left(x1 + \left(x1 \cdot \left(x1 \cdot x1\right) + \left(\left(\left(\frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + x2 \cdot 2.0\right) - x1}{1.0 + x1 \cdot x1} - 3.0\right) \cdot \left(\left(x1 \cdot 2.0\right) \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + x2 \cdot 2.0\right) - x1}{1.0 + x1 \cdot x1}\right) + \left(\left(x1 \cdot x1\right) \cdot \mathsf{fma}\left(6.0, -1, 6.0\right) + \left(\left(-x1\right) \cdot \left(x1 \cdot 6.0\right) + \left(4.0 \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + x2 \cdot 2.0\right) - x1}{1.0 + x1 \cdot x1}\right) \cdot \left(x1 \cdot x1\right)\right)\right)\right) \cdot \left(1.0 + x1 \cdot x1\right) + \left(\left(3.0 \cdot x1\right) \cdot x1\right) \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + x2 \cdot 2.0\right) - x1}{1.0 + x1 \cdot x1}\right)\right)\right) + \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 - x2 \cdot 2.0\right) - x1}{1.0 + x1 \cdot x1} \cdot 3.0\right)
double f(double x1, double x2) {
        double r2846959 = x1;
        double r2846960 = 2.0;
        double r2846961 = r2846960 * r2846959;
        double r2846962 = 3.0;
        double r2846963 = r2846962 * r2846959;
        double r2846964 = r2846963 * r2846959;
        double r2846965 = x2;
        double r2846966 = r2846960 * r2846965;
        double r2846967 = r2846964 + r2846966;
        double r2846968 = r2846967 - r2846959;
        double r2846969 = r2846959 * r2846959;
        double r2846970 = 1.0;
        double r2846971 = r2846969 + r2846970;
        double r2846972 = r2846968 / r2846971;
        double r2846973 = r2846961 * r2846972;
        double r2846974 = r2846972 - r2846962;
        double r2846975 = r2846973 * r2846974;
        double r2846976 = 4.0;
        double r2846977 = r2846976 * r2846972;
        double r2846978 = 6.0;
        double r2846979 = r2846977 - r2846978;
        double r2846980 = r2846969 * r2846979;
        double r2846981 = r2846975 + r2846980;
        double r2846982 = r2846981 * r2846971;
        double r2846983 = r2846964 * r2846972;
        double r2846984 = r2846982 + r2846983;
        double r2846985 = r2846969 * r2846959;
        double r2846986 = r2846984 + r2846985;
        double r2846987 = r2846986 + r2846959;
        double r2846988 = r2846964 - r2846966;
        double r2846989 = r2846988 - r2846959;
        double r2846990 = r2846989 / r2846971;
        double r2846991 = r2846962 * r2846990;
        double r2846992 = r2846987 + r2846991;
        double r2846993 = r2846959 + r2846992;
        return r2846993;
}
↓
double f(double x1, double x2) {
        double r2846994 = x1;
        double r2846995 = r2846994 * r2846994;
        double r2846996 = r2846994 * r2846995;
        double r2846997 = 3.0;
        double r2846998 = r2846997 * r2846994;
        double r2846999 = r2846998 * r2846994;
        double r2847000 = x2;
        double r2847001 = 2.0;
        double r2847002 = r2847000 * r2847001;
        double r2847003 = r2846999 + r2847002;
        double r2847004 = r2847003 - r2846994;
        double r2847005 = 1.0;
        double r2847006 = r2847005 + r2846995;
        double r2847007 = r2847004 / r2847006;
        double r2847008 = r2847007 - r2846997;
        double r2847009 = r2846994 * r2847001;
        double r2847010 = r2847009 * r2847007;
        double r2847011 = r2847008 * r2847010;
        double r2847012 = 6.0;
        double r2847013 = -1.0;
        double r2847014 = fma(r2847012, r2847013, r2847012);
        double r2847015 = r2846995 * r2847014;
        double r2847016 = -r2846994;
        double r2847017 = r2846994 * r2847012;
        double r2847018 = r2847016 * r2847017;
        double r2847019 = 4.0;
        double r2847020 = r2847019 * r2847007;
        double r2847021 = r2847020 * r2846995;
        double r2847022 = r2847018 + r2847021;
        double r2847023 = r2847015 + r2847022;
        double r2847024 = r2847011 + r2847023;
        double r2847025 = r2847024 * r2847006;
        double r2847026 = r2846999 * r2847007;
        double r2847027 = r2847025 + r2847026;
        double r2847028 = r2846996 + r2847027;
        double r2847029 = r2846994 + r2847028;
        double r2847030 = r2846999 - r2847002;
        double r2847031 = r2847030 - r2846994;
        double r2847032 = r2847031 / r2847006;
        double r2847033 = r2847032 * r2846997;
        double r2847034 = r2847029 + r2847033;
        double r2847035 = r2846994 + r2847034;
        return r2847035;
}

Error

Bits error versus x1

Bits error versus x2

Derivation

  1. Initial program 0.5

    \[x1 + \left(\left(\left(\left(\left(\left(\left(2.0 \cdot x1\right) \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right) \cdot \left(\frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0} - 3.0\right) + \left(x1 \cdot x1\right) \cdot \left(4.0 \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0} - 6.0\right)\right) \cdot \left(x1 \cdot x1 + 1.0\right) + \left(\left(3.0 \cdot x1\right) \cdot x1\right) \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right) + \left(x1 \cdot x1\right) \cdot x1\right) + x1\right) + 3.0 \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 - 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right)\]
  2. Using strategy rm

  3. Applied add-cube-cbrt0.6

    \[\leadsto x1 + \left(\left(\left(\left(\left(\left(\left(2.0 \cdot x1\right) \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right) \cdot \left(\frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0} - 3.0\right) + \left(x1 \cdot x1\right) \cdot \left(4.0 \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0} - \color{blue}{\left(\sqrt[3]{6.0} \cdot \sqrt[3]{6.0}\right) \cdot \sqrt[3]{6.0}}\right)\right) \cdot \left(x1 \cdot x1 + 1.0\right) + \left(\left(3.0 \cdot x1\right) \cdot x1\right) \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right) + \left(x1 \cdot x1\right) \cdot x1\right) + x1\right) + 3.0 \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 - 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right)\]

  4. Applied prod-diff0.6

    \[\leadsto x1 + \left(\left(\left(\left(\left(\left(\left(2.0 \cdot x1\right) \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right) \cdot \left(\frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0} - 3.0\right) + \left(x1 \cdot x1\right) \cdot \color{blue}{\left(\mathsf{fma}\left(4.0, \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}, -\sqrt[3]{6.0} \cdot \left(\sqrt[3]{6.0} \cdot \sqrt[3]{6.0}\right)\right) + \mathsf{fma}\left(-\sqrt[3]{6.0}, \sqrt[3]{6.0} \cdot \sqrt[3]{6.0}, \sqrt[3]{6.0} \cdot \left(\sqrt[3]{6.0} \cdot \sqrt[3]{6.0}\right)\right)\right)}\right) \cdot \left(x1 \cdot x1 + 1.0\right) + \left(\left(3.0 \cdot x1\right) \cdot x1\right) \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right) + \left(x1 \cdot x1\right) \cdot x1\right) + x1\right) + 3.0 \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 - 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right)\]

  5. Applied distribute-lft-in0.6

    \[\leadsto x1 + \left(\left(\left(\left(\left(\left(\left(2.0 \cdot x1\right) \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right) \cdot \left(\frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0} - 3.0\right) + \color{blue}{\left(\left(x1 \cdot x1\right) \cdot \mathsf{fma}\left(4.0, \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}, -\sqrt[3]{6.0} \cdot \left(\sqrt[3]{6.0} \cdot \sqrt[3]{6.0}\right)\right) + \left(x1 \cdot x1\right) \cdot \mathsf{fma}\left(-\sqrt[3]{6.0}, \sqrt[3]{6.0} \cdot \sqrt[3]{6.0}, \sqrt[3]{6.0} \cdot \left(\sqrt[3]{6.0} \cdot \sqrt[3]{6.0}\right)\right)\right)}\right) \cdot \left(x1 \cdot x1 + 1.0\right) + \left(\left(3.0 \cdot x1\right) \cdot x1\right) \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right) + \left(x1 \cdot x1\right) \cdot x1\right) + x1\right) + 3.0 \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 - 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right)\]

  6. Simplified0.6

    \[\leadsto x1 + \left(\left(\left(\left(\left(\left(\left(2.0 \cdot x1\right) \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right) \cdot \left(\frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0} - 3.0\right) + \left(\left(x1 \cdot x1\right) \cdot \mathsf{fma}\left(4.0, \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}, -\sqrt[3]{6.0} \cdot \left(\sqrt[3]{6.0} \cdot \sqrt[3]{6.0}\right)\right) + \color{blue}{\left(x1 \cdot x1\right) \cdot \mathsf{fma}\left(6.0, -1, 6.0\right)}\right)\right) \cdot \left(x1 \cdot x1 + 1.0\right) + \left(\left(3.0 \cdot x1\right) \cdot x1\right) \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right) + \left(x1 \cdot x1\right) \cdot x1\right) + x1\right) + 3.0 \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 - 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right)\]
  7. Using strategy rm

  8. Applied fma-udef0.6

    \[\leadsto x1 + \left(\left(\left(\left(\left(\left(\left(2.0 \cdot x1\right) \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right) \cdot \left(\frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0} - 3.0\right) + \left(\left(x1 \cdot x1\right) \cdot \color{blue}{\left(4.0 \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0} + \left(-\sqrt[3]{6.0} \cdot \left(\sqrt[3]{6.0} \cdot \sqrt[3]{6.0}\right)\right)\right)} + \left(x1 \cdot x1\right) \cdot \mathsf{fma}\left(6.0, -1, 6.0\right)\right)\right) \cdot \left(x1 \cdot x1 + 1.0\right) + \left(\left(3.0 \cdot x1\right) \cdot x1\right) \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right) + \left(x1 \cdot x1\right) \cdot x1\right) + x1\right) + 3.0 \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 - 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right)\]

  9. Applied distribute-rgt-in0.6

    \[\leadsto x1 + \left(\left(\left(\left(\left(\left(\left(2.0 \cdot x1\right) \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right) \cdot \left(\frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0} - 3.0\right) + \left(\color{blue}{\left(\left(4.0 \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right) \cdot \left(x1 \cdot x1\right) + \left(-\sqrt[3]{6.0} \cdot \left(\sqrt[3]{6.0} \cdot \sqrt[3]{6.0}\right)\right) \cdot \left(x1 \cdot x1\right)\right)} + \left(x1 \cdot x1\right) \cdot \mathsf{fma}\left(6.0, -1, 6.0\right)\right)\right) \cdot \left(x1 \cdot x1 + 1.0\right) + \left(\left(3.0 \cdot x1\right) \cdot x1\right) \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right) + \left(x1 \cdot x1\right) \cdot x1\right) + x1\right) + 3.0 \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 - 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right)\]

  10. Simplified0.5

    \[\leadsto x1 + \left(\left(\left(\left(\left(\left(\left(2.0 \cdot x1\right) \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right) \cdot \left(\frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0} - 3.0\right) + \left(\left(\left(4.0 \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right) \cdot \left(x1 \cdot x1\right) + \color{blue}{\left(\left(-6.0\right) \cdot x1\right) \cdot x1}\right) + \left(x1 \cdot x1\right) \cdot \mathsf{fma}\left(6.0, -1, 6.0\right)\right)\right) \cdot \left(x1 \cdot x1 + 1.0\right) + \left(\left(3.0 \cdot x1\right) \cdot x1\right) \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right) + \left(x1 \cdot x1\right) \cdot x1\right) + x1\right) + 3.0 \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 - 2.0 \cdot x2\right) - x1}{x1 \cdot x1 + 1.0}\right)\]

  11. Final simplification0.5

    \[\leadsto x1 + \left(\left(x1 + \left(x1 \cdot \left(x1 \cdot x1\right) + \left(\left(\left(\frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + x2 \cdot 2.0\right) - x1}{1.0 + x1 \cdot x1} - 3.0\right) \cdot \left(\left(x1 \cdot 2.0\right) \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + x2 \cdot 2.0\right) - x1}{1.0 + x1 \cdot x1}\right) + \left(\left(x1 \cdot x1\right) \cdot \mathsf{fma}\left(6.0, -1, 6.0\right) + \left(\left(-x1\right) \cdot \left(x1 \cdot 6.0\right) + \left(4.0 \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + x2 \cdot 2.0\right) - x1}{1.0 + x1 \cdot x1}\right) \cdot \left(x1 \cdot x1\right)\right)\right)\right) \cdot \left(1.0 + x1 \cdot x1\right) + \left(\left(3.0 \cdot x1\right) \cdot x1\right) \cdot \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 + x2 \cdot 2.0\right) - x1}{1.0 + x1 \cdot x1}\right)\right)\right) + \frac{\left(\left(3.0 \cdot x1\right) \cdot x1 - x2 \cdot 2.0\right) - x1}{1.0 + x1 \cdot x1} \cdot 3.0\right)\]

Reproduce

herbie shell --seed 2019165 +o rules:numerics
(FPCore (x1 x2)
  :name "Rosa's FloatVsDoubleBenchmark"
  (+ x1 (+ (+ (+ (+ (* (+ (* (* (* 2.0 x1) (/ (- (+ (* (* 3.0 x1) x1) (* 2.0 x2)) x1) (+ (* x1 x1) 1.0))) (- (/ (- (+ (* (* 3.0 x1) x1) (* 2.0 x2)) x1) (+ (* x1 x1) 1.0)) 3.0)) (* (* x1 x1) (- (* 4.0 (/ (- (+ (* (* 3.0 x1) x1) (* 2.0 x2)) x1) (+ (* x1 x1) 1.0))) 6.0))) (+ (* x1 x1) 1.0)) (* (* (* 3.0 x1) x1) (/ (- (+ (* (* 3.0 x1) x1) (* 2.0 x2)) x1) (+ (* x1 x1) 1.0)))) (* (* x1 x1) x1)) x1) (* 3.0 (/ (- (- (* (* 3.0 x1) x1) (* 2.0 x2)) x1) (+ (* x1 x1) 1.0))))))